/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Copyright (C) 2019-2020. Huawei Technologies Co., Ltd. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_TF_ADAPTER_KERNELS_THREAD_POOL_H
#define TENSORFLOW_TF_ADAPTER_KERNELS_THREAD_POOL_H

#include <atomic>
#include <vector>
#include <queue>
#include <memory>
#include <thread>
#include <mutex>
#include <condition_variable>
#include <future>
#include <functional>
#include <stdexcept>
#include "tensorflow/core/platform/logging.h"

class ThreadPool {
 public:
  template<class F, class... Args>
  auto Enqueue(F&& f, Args&&... args)
    -> std::future<typename std::result_of<F(Args...)>::type>;
  // initialize thread pool
  void InitThreadPool(size_t threads);
  // ThreadPool construct
  ThreadPool() : stop_(false), init_flag_(false) {}
  // ThreadPool destruct
  ~ThreadPool();
 private:
  // need to keep track of threads so we can join them
  std::vector< std::thread > workers_;
  // the task queue
  std::queue< std::function<void()> > tasks_;
  std::mutex queue_mutex_;
  std::condition_variable condition_;
  bool stop_;
  std::atomic<bool> init_flag_;
};

// launch some amount of workers_
void ThreadPool::InitThreadPool(size_t threads)
{
  if (!init_flag_) {
    for (size_t i = 0; i < threads; ++i) {
      workers_.emplace_back([this] {
        for (;;) {
          std::function<void()> task;
          {
            std::unique_lock<std::mutex> lock(this->queue_mutex_);
            this->condition_.wait(lock,
                [this] { return this->stop_ || !this->tasks_.empty(); });
            if (this->stop_ || this->tasks_.empty()) { return; }
            task = std::move(this->tasks_.front());
            this->tasks_.pop();
          }
          task();
        }
      });
    }
  }
  init_flag_ = true;
}

// add new work item to the pool
template<class F, class... Args>
auto ThreadPool::Enqueue(F&& f, Args&&... args)
  -> std::future<typename std::result_of<F(Args...)>::type>
{
  if (!init_flag_) { LOG(ERROR) << "thread pool is not initialized."; }
  using return_type = typename std::result_of<F(Args...)>::type;
  auto task = std::make_shared< std::packaged_task<return_type()> >(
      std::bind(std::forward<F>(f), std::forward<Args>(args)...));
  std::future<return_type> res = task->get_future();
  {
    std::unique_lock<std::mutex> lock(queue_mutex_);
    if (stop_) { LOG(ERROR) << "Enqueue on stopped ThreadPool."; }
    tasks_.emplace([task]() { (*task)(); });
  }
  condition_.notify_one();
  return res;
}

ThreadPool::~ThreadPool()
{
  {
    std::unique_lock<std::mutex> lock(queue_mutex_);
    stop_ = true;
  }
  init_flag_ = false;
  condition_.notify_all();
  for (std::thread &worker : workers_) { worker.join(); }
}

#endif